Field of the Invention
The present invention relates generally to computer science and, more specifically, to predicting unscheduled events in manufacturing.
Description of the Related Art
The classical breakdown of economic sectors fall into the retrieval and production of raw materials, such as food and iron; the transformation of the raw materials into intermediate materials or goods, such as computers, vehicles and clothing; and supplying services to consumers such as legal or bank. At least the first two sectors rely on machines to produce or fabricate the goods which enter the marketplace. Time and materials are a major consideration in the added costs of goods entering the market place. These added costs come from materials wasted due to defects and time spent in both fabricating and maintaining the equipment for fabrication.
Reducing wasted material and time spent on producing goods directly affect the cost of manufacturing and benefit the industry and consumer alike. The time spent on producing goods may include scheduled downtime for maintaining the equipment or unscheduled downtime in response to unexpected or unforeseen failures in the manufacturing equipment or process or a component of either. Unscheduled downtime is a major source of lost revenue in all of manufacturing. Conventionally, manufacturers have scheduled maintenance to reduce material defects associated with worn equipment and unscheduled downtime due to equipment failure. The latter can result in lost and more variable productivity, lower and more variable product quality, higher replacement part inventory costs, higher repair human resourcing costs, product scrap, and costly damage to equipment and equipment components.
With the on-going pressures of lowering cost, improving quality and reducing variability in the face of larger wafers and smaller feature sizes, the nano-manufacturing industry has begun to embrace a move from a reactive to predictive paradigm of operation. Predictive capabilities such as predictive maintenance (PdM) are cited by the International Technology Roadmap for Semiconductors (ITRS) as critical technologies to incorporate into production, with PdM identified as a key component to reduce unscheduled downtime, maintain high quality, and reduce cost. Predictive maintenance, also called Predictive and Preventative Maintenance (PPM), is the technique of relating facility state information to maintenance information to predict the need for maintenance events that will alleviate unscheduled downtime or reduce the frequency of scheduled downtime in conservative downtime scheduling situations.
A key barrier to the effective implementation of predictive technologies (including PdM, predictive scheduling, predictive yield and virtual metrology) is an ability to guarantee to some level that the benefits to be provided by the technology will outweigh the operational costs. FIG. 1 is a hybrid of a bar chart/line graph 100 illustrating the cost benefit 180 for Predictive and Preventative Maintenance. The graph 100 illustrates the costs 180 for a conventional method 110 and an aggressive method 120 for performing preventative maintenance. The cost 180 for the conventional method 110 has a larger scheduled downtime 104, a smaller production 102, and a smaller unscheduled downtime 106 than the aggressive method 120. A cost 118 for the conventional method 110 is less than a cost 128 for the aggressive method 120 even with the greater production 102. Thus, the costs 118 of avoiding unscheduled downtime 104 by scheduling downtime 104 will out-weight the costs 128 associated with greater production 102 and greater unscheduled downtime 106. To reduce a cost 138 further, a proper balance may be determined for a new and improved method 130 by predicting unscheduled downtime 106 prior to it occurring.
Therefore, there is a need for an improved method for determining when unscheduled downtime may occur before it actually occurs for substantially reducing associated costs.